[1]} QMIX is an algorithm designed for training Q-learning agents in cooperative environments. As with COMA, it tries to solve credit assignment using additional information during training. As agents still need the Q-function during execution, we cannot alter its input as in COMA. QMIX proposes to use a mixer function to combine Q-functions of agents into a centralized Q-function that is trained on global reward. QMIX ascertains that mixer function ensures monotonicity in its inputs, which makes the argmax of the agents' Q-functions consistent with the argmax of the centralized Q-function. This property is called Individual-Global-Max (IGM) and is essential for factorizing the global Q-function to agents' Q-Functions.
